Viability assessment is essential for surgery and therapy planning following a heart attack. In particular, the proportion of viable myocardium is a major factor in determining whether a patient may benefit from a revascularization procedure. In addition to estimating the left ventricle thickness and thickening, it is possible to visualize normal, ischemic, and non-viable areas with high spatial resolution, using contrast-enhanced imaging techniques and particularly late-enhancement cardiac magnetic resonance (LECMR). To locate and quantify non-viable tissues, it is important to delineate the endo- and epicardial contours on all available views of the heart. In particular, the contours obtained on long-axis acquisitions provide information which is complementary to the information obtained from short-axis data, because the spacing between short-axis slices is too large (up to 10 mm) to reconstruct an accurate 3D heart volume for viability assessment.
Designing an automatic method to delineate the endo- and epicardial contours is difficult, mainly because of the non-homogeneous aspect of the myocardium, resulting from contrast agent accumulation in ischemic and non-viable areas. These regions appear in white while the healthy parts are dark and the surrounding organs vary from grey to dark. FIG. 1 offers two examples of LECMR long axis (LA) 2-chamber views obtained from two patients, showing a healthy myocardium (11), the blood pool (12) and abnormal tissues (13). The challenge is thus to extract a structure containing both dark and white areas from a textured environment. Moreover, the borders of the white regions often appear very fuzzy, especially if they are close to the blood pool, which makes it particularly difficult to correctly locate the endocardium. While these difficulties are also encountered when processing short-axis (SA) data, there is an additional difficulty with LA views: the myocardium does not appear with an easily detectable ring-shape; it is thus necessary to find a new way to locate it before finding its contours. Even though a number of scientific publications deal with the segmentation of long-axis images (using only LA data: M. Blok, M. G. Danilouchkine, C. J. Veenman, F. Admiraal-Behloul, E. A. Hendriks, J. H. C. Reiber and B. P. F. Lelieveldt, “Long-axis cardiac MRI contour detection with adaptive virtual exploring robot”, Proceedings of the Third International Conference on Functional Imaging and Modeling of the Heart (FIMH'2005), pp. 54-64, Springer, 2005; using LA and SA data: Koikkalainen, M. Pollari, J. Lötjönen, S. Kivistö and K. Lauerma, “Segmentation of cardiac structures simultaneously from short- and long-axis MR images”, Proceedings of the 7th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'04), pp. 427-434, Springer, 2004), none of them makes full use of the LECMR tissue viability data.